OpenRaiser/Intern-Atlas huggingface dataset:
Secure Full-Duplex Device-to-Device Communication
Venue: arXiv cs.IT 2017
Year: 2017

This paper considers full-duplex (FD) device-to-device (D2D) communications in a downlink MISO cellular system in the presence of multiple eavesdroppers. The D2D pair communicate sharing the same frequency band allocated to the cellular users (CUs). Since the D2D users share the same frequency as the CUs, both the base station (BS) and D2D transmissions interfere each other. In addition, due to limited processing capability, D2D users are susceptible to external attacks. Our aim is to design optimal beamforming and power control mechanism to guarantee secure communication while delivering the required quality-of-service (QoS) for the D2D link. In order to improve security, artificial noise (AN) is transmitted by the BS. We design robust beamforming for secure message as well as the AN in the worst-case sense for minimizing total transmit power with imperfect channel state information (CSI) of all links available at the BS. The problem is strictly non-convex with infinitely many constraints. By discovering the hidden convexity of the problem, we derive a rank-one optimal solution for the power minimization problem.
A Deep Reinforcement Learning Chatbot
Venue: arXiv cs.CL 2017
Year: 2017

We present MILABOT: a deep reinforcement learning chatbot developed by the Montreal Institute for Learning Algorithms (MILA) for the Amazon Alexa Prize competition. MILABOT is capable of conversing with humans on popular small talk topics through both speech and text. The system consists of an ensemble of natural language generation and retrieval models, including template-based models, bag-of-words models, sequence-to-sequence neural network and latent variable neural network models. By applying reinforcement learning to crowdsourced data and real-world user interactions, the system has been trained to select an appropriate response from the models in its ensemble. The system has been evaluated through A/B testing with real-world users, where it performed significantly better than many competing systems. Due to its machine learning architecture, the system is likely to improve with additional data.
Resolving API Mentions in Informal Documents
Venue: arXiv cs.SE 2017
Year: 2017

Developer forums contain opinions and information related to the usage of APIs. API names in forum posts are often not explicitly linked to their official resources. Automatic linking of an API mention to its official resources can be challenging for various reasons, such as, name overloading. We present a technique, ANACE, to automatically resolve API mentions in the textual contents of forum posts. Given a database of APIs, we first detect all words in a forum post that are potential references to an API. We then use a combination of heuristics and machine learning to eliminate false positives and to link true positives to the actual APIs and their resources.
Millimeter Wave Multi-Beam-Switching Antenna
Venue: arXiv cs.IT 2017
Year: 2017

The paper introduces a high gain end-fire printed antenna operating at 14 GHz with a bandwidth of 2 GHz. The 3x3 antenna sub-array formed of this element is used to present its beam steering and multi-beam-switching capabilities for millimeter wave Massive MIMO antenna arrays of 5G networks. The co-existence of these two techniques in a Massive MIMO sub-array, aided by the use of a simple two vector excitation matrix technique, is shown in this paper. The high gain and small form factor of the antenna is useful in applications, like 5G arrays, where space is premium and the inherent layered structure also aids to the better cooling of the antenna front end.
Parameterization of SWIM Mobility Model Using Contact Traces
Venue: arXiv cs.NI 2017
Year: 2017

Opportunistic networks (OppNets) are focused to exploit direct, localised communications which occur in a peer-to-peer manner mostly based on people's movements and their contact durations. Therefore, the use of realistic mobility models is critical to evaluate the data dissemination in OppNets. One of the mobility models that is available in OMNeT++ which can be used to mimic human movement patterns is Small Worlds in Motion (SWIM). The SWIM model is based on the intuition that humans often visit nearby locations and if the visited location is far away, then it is probably due to the popularity of the location. As an alternative to mobility of a node, pairwise contact probabilities are also used to evaluate the data dissemination in OppNets. Pairwise contact probabilities can be used to predict that a node will be met by a particular node. These probabilities can be derived in many ways. One of the ways is to calculate the average probability with which a node will meet another particular node at any point of time. Another way is to calculate the probability with which a node will meet another based on the time of day. The way of calculating pairwise contact probability depends on the scenario. In this work, the pairwise contact probabilities obtained from the real traces are used to tune the parameters of the SWIM mobility model. The traces and the SWIM model are compared in terms of contact durations, inter-contact times and, number of pairwise contacts. How to decide SWIM parameters using real contact traces are being addressed.
Optimal-Dimensionality Sampling on the Sphere: Improvements and Variations
Venue: arXiv cs.IT 2017
Year: 2017

For the accurate representation and reconstruction of band-limited signals on the sphere, an optimal-dimensionality sampling scheme has been recently proposed which requires the optimal number of samples equal to the number of degrees of freedom of the signal in the spectral (harmonic) domain. The computation of the spherical harmonic transform (SHT) associated with the optimal-dimensionality sampling requires the inversion of a series of linear systems in an iterative manner. The stability of the inversion depends on the placement of iso-latitude rings of samples along co-latitude. In this work, we have developed a method to place these iso-latitude rings of samples with the objective of improving the well-conditioning of the linear systems involved in the computation of the SHT. We also propose a multi-pass SHT algorithm to iteratively improve the accuracy of the SHT of band-limited signals. Furthermore, we review the changes in the computational complexity and improvement in accuracy of the SHT with the embedding of the proposed methods. Through numerical experiments, we illustrate that the proposed variations and improvements in the SHT algorithm corresponding to the optimal-dimensionality sampling scheme significantly enhance the accuracy of the SHT.
Adaptive Processing of Spatial-Keyword Data Over a Distributed Streaming Cluster
Venue: arXiv cs.DC 2017
Year: 2017

The widespread use of GPS-enabled smartphones along with the popularity of micro-blogging and social networking applications, e.g., Twitter and Facebook, has resulted in the generation of huge streams of geo-tagged textual data. Many applications require real-time processing of these streams. For example, location-based e-coupon and ad-targeting systems enable advertisers to register millions of ads to millions of users. The number of users is typically very high and they are continuously moving, and the ads change frequently as well. Hence sending the right ad to the matching users is very challenging. Existing streaming systems are either centralized or are not spatial-keyword aware, and cannot efficiently support the processing of rapidly arriving spatial-keyword data streams. This paper presents Tornado, a distributed spatial-keyword stream processing system. Tornado features routing units to fairly distribute the workload, and furthermore, co-locate the data objects and the corresponding queries at the same processing units. The routing units use the Augmented-Grid, a novel structure that is equipped with an efficient search algorithm for distributing the data objects and queries. Tornado uses evaluators to process the data objects against the queries. The routing units minimize the redundant communication by not sending data updates for processing when these updates do not match any query. By applying dynamically evaluated cost formulae that continuously represent the processing overhead at each evaluator, Tornado is adaptive to changes in the workload. Extensive experimental evaluation using spatio-textual range queries over real Twitter data indicates that Tornado outperforms the non-spatio-textually aware approaches by up to two orders of magnitude in terms of the overall system throughput.
Segmentation and Classification of Cine-MR Images Using Fully Convolutional Networks and Handcrafted Features
Venue: arXiv cs.CV 2017
Year: 2017

Three-dimensional cine-MRI is of crucial importance for assessing the cardiac function. Features that describe the anatomy and function of cardiac structures (e.g. Left Ventricle (LV), Right Ventricle (RV), and Myocardium(MC)) are known to have significant diagnostic value and can be computed from 3D cine-MR images. However, these features require precise segmentation of cardiac structures. Among the fully automated segmentation methods, Fully Convolutional Networks (FCN) with Skip Connections have shown robustness in medical segmentation problems. In this study, we develop a complete pipeline for classification of subjects with cardiac conditions based on 3D cine-MRI. For the segmentation task, we develop a 2D FCN and introduce Parallel Paths (PP) as a way to exploit the 3D information of the cine-MR image. For the classification task, 125 features were extracted from the segmented structures, describing their anatomy and function. Next, a two-stage pipeline for feature selection using the LASSO method is developed. A subset of 20 features is selected for classification. Each subject is classified using an ensemble of Logistic Regression, Multi-Layer Perceptron, and Support Vector Machine classifiers through majority voting. The Dice Coefficient for segmentation was 0.95+-0.03, 0.89+-0.13, and 0.90+-0.03 for LV, RV, and MC respectively. The 8-fold cross validation accuracy for the classification task was 95.05% and 92.77% based on ground truth and the proposed methods segmentations respectively. The results show that the PPs increase the segmentation accuracy, by exploiting the spatial relations. Moreover, the classification algorithm and the features showed discriminability while keeping the sensitivity to segmentation error as low as possible.
EMM: Energy-Aware Mobility Management for Mobile Edge Computing in Ultra Dense Networks
Venue: arXiv cs.IT 2017
Year: 2017

Merging mobile edge computing (MEC) functionality with the dense deployment of base stations (BSs) provides enormous benefits such as a real proximity, low latency access to computing resources. However, the envisioned integration creates many new challenges, among which mobility management (MM) is a critical one. Simply applying existing radio access oriented MM schemes leads to poor performance mainly due to the co-provisioning of radio access and computing services of the MEC-enabled BSs. In this paper, we develop a novel user-centric energy-aware mobility management (EMM) scheme, in order to optimize the delay due to both radio access and computation, under the long-term energy consumption constraint of the user. Based on Lyapunov optimization and multi-armed bandit theories, EMM works in an online fashion without future system state information, and effectively handles the imperfect system state information. Theoretical analysis explicitly takes radio handover and computation migration cost into consideration and proves a bounded deviation on both the delay performance and energy consumption compared to the oracle solution with exact and complete future system information. The proposed algorithm also effectively handles the scenario in which candidate BSs randomly switch on/off during the offloading process of a task. Simulations show that the proposed algorithms can achieve close-to-optimal delay performance while satisfying the user energy consumption constraint.
A geometric approach for learning compliant motions from demonstration
Venue: arXiv cs.RO 2017
Year: 2017

This paper proposes a method to learn from human demonstration compliant contact motions, which take advantage of interaction forces between workpieces to align them, even when contact force may occur from different directions on different instances of reproduction. To manage the uncertainty in unstructured conditions, the motions learned with our method can be reproduced with an impedance controller. Learning from Demonstration is used because the planning of compliant motions in 3-D is computationally intractable. The proposed method will learn an individual compliant motion, many of which can be combined to solve more complex tasks. The method is based on measuring simultaneously the direction of motion and the forces acting on the end-effector. From these measurements we construct a set of constraints for motion directions which, with correct compliance, result in the observed motion. Constraints from multiple demonstrations are projected into a 2-D angular coordinate system where their intersection is determined to find a set of feasible desired directions, of which a single motion direction is chosen. The work is based on the assumption that movement in directions other than the desired direction is caused by interaction forces. Using this assumption, we infer the number of compliant axes and, if required, their directions. Experiments with a KUKA LWR4+ show that our method can successfully reproduce motions which require taking advantage of the environment.
Objectness Scoring and Detection Proposals in Forward-Looking Sonar Images with Convolutional Neural Networks
Venue: arXiv cs.CV 2017
Year: 2017

Forward-looking sonar can capture high resolution images of underwater scenes, but their interpretation is complex. Generic object detection in such images has not been solved, specially in cases of small and unknown objects. In comparison, detection proposal algorithms have produced top performing object detectors in real-world color images. In this work we develop a Convolutional Neural Network that can reliably score objectness of image windows in forward-looking sonar images and by thresholding objectness, we generate detection proposals. In our dataset of marine garbage objects, we obtain 94% recall, generating around 60 proposals per image. The biggest strength of our method is that it can generalize to previously unseen objects. We show this by detecting chain links, walls and a wrench without previous training in such objects. We strongly believe our method can be used for class-independent object detection, with many real-world applications such as chain following and mine detection.
Vessel Segmentation and Catheter Detection in X-Ray Angiograms Using Superpixels
Venue: arXiv cs.CV 2017
Year: 2017

Coronary artery disease (CAD) is the leading causes of death around the world. One of the most common imaging methods for diagnosing this disease is X-ray angiography. Diagnosing using these images is usually challenging due to non-uniform illumination, low contrast, presence of other body tissues, presence of catheter etc. These challenges make the diagnoses task of cardiologists tougher and more prone to misdiagnosis. In this paper we propose a new automated framework for coronary arteries segmentation, catheter detection and center-line extraction in x-ray angiography images. Our proposed segmentation method is based on superpixels. In this method at first three different superpixel scales are exploited and a measure for vesselness probability of each superpixel is determined. A majority voting is used for obtaining an initial segmentation map from these three superpixel scales. This initial segmentation is refined by finding the orthogonal line on each ridge pixel of vessel region. In this framework we use our catheter detection and tracking method which detects the catheter by finding its ridge in the first frame and traces in other frames by fitting a second order polynomial on it. Also we use the image ridges for extracting the coronary arteries centerlines. We evaluated our method qualitatively and quantitatively on two different challenging datasets and compared it with one of the previous well-known coronary arteries segmentation methods. Our method could detect the catheter and reduced the false positive rate in addition to achieving better segmentation results. The evaluation results prove that our method performs better in a much shorter time.
Privacy Loss in Apple's Implementation of Differential Privacy on MacOS 10.12
Venue: arXiv cs.CR 2017
Year: 2017

In June 2016, Apple announced that it will deploy differential privacy for some user data collection in order to ensure privacy of user data, even from Apple. The details of Apple's approach remained sparse. Although several patents have since appeared hinting at the algorithms that may be used to achieve differential privacy, they did not include a precise explanation of the approach taken to privacy parameter choice. Such choice and the overall approach to privacy budget use and management are key questions for understanding the privacy protections provided by any deployment of differential privacy. In this work, through a combination of experiments, static and dynamic code analysis of macOS Sierra (Version 10.12) implementation, we shed light on the choices Apple made for privacy budget management. We discover and describe Apple's set-up for differentially private data processing, including the overall data pipeline, the parameters used for differentially private perturbation of each piece of data, and the frequency with which such data is sent to Apple's servers. We find that although Apple's deployment ensures that the (differential) privacy loss per each datum submitted to its servers is $1$ or $2$, the overall privacy loss permitted by the system is significantly higher, as high as $16$ per day for the four initially announced applications of Emojis, New words, Deeplinks and Lookup Hints. Furthermore, Apple renews the privacy budget available every day, which leads to a possible privacy loss of 16 times the number of days since user opt-in to differentially private data collection for those four applications. We advocate that in order to claim the full benefits of differentially private data collection, Apple must give full transparency of its implementation, enable user choice in areas related to privacy loss, and set meaningful defaults on the privacy loss permitted.
An Adaptive Sampling Scheme to Efficiently Train Fully Convolutional Networks for Semantic Segmentation
Venue: arXiv cs.CV 2017
Year: 2017

Deep convolutional neural networks (CNNs) have shown excellent performance in object recognition tasks and dense classification problems such as semantic segmentation. However, training deep neural networks on large and sparse datasets is still challenging and can require large amounts of computation and memory. In this work, we address the task of performing semantic segmentation on large data sets, such as three-dimensional medical images. We propose an adaptive sampling scheme that uses a-posterior error maps, generated throughout training, to focus sampling on difficult regions, resulting in improved learning. Our contribution is threefold: 1) We give a detailed description of the proposed sampling algorithm to speed up and improve learning performance on large images. We propose a deep dual path CNN that captures information at fine and coarse scales, resulting in a network with a large field of view and high resolution outputs. We show that our method is able to attain new state-of-the-art results on the VISCERAL Anatomy benchmark.
Globally Normalized Reader
Venue: arXiv cs.CL 2017
Year: 2017

Rapid progress has been made towards question answering (QA) systems that can extract answers from text. Existing neural approaches make use of expensive bi-directional attention mechanisms or score all possible answer spans, limiting scalability. We propose instead to cast extractive QA as an iterative search problem: select the answer's sentence, start word, and end word. This representation reduces the space of each search step and allows computation to be conditionally allocated to promising search paths. We show that globally normalizing the decision process and back-propagating through beam search makes this representation viable and learning efficient. We empirically demonstrate the benefits of this approach using our model, Globally Normalized Reader (GNR), which achieves the second highest single model performance on the Stanford Question Answering Dataset (68.4 EM, 76.21 F1 dev) and is 24.7x faster than bi-attention-flow. We also introduce a data-augmentation method to produce semantically valid examples by aligning named entities to a knowledge base and swapping them with new entities of the same type. This method improves the performance of all models considered in this work and is of independent interest for a variety of NLP tasks.
CLaC at SemEval-2016 Task 11: Exploring linguistic and psycho-linguistic Features for Complex Word Identification
Venue: arXiv cs.CL 2017
Year: 2017

This paper describes the system deployed by the CLaC-EDLK team to the "SemEval 2016, Complex Word Identification task". The goal of the task is to identify if a given word in a given context is "simple" or "complex". Our system relies on linguistic features and cognitive complexity. We used several supervised models, however the Random Forest model outperformed the others. Overall our best configuration achieved a G-score of 68.8% in the task, ranking our system 21 out of 45.
Uncertainty measurement with belief entropy on interference effect in Quantum-Like Bayesian Networks
Venue: arXiv cs.AI 2017
Year: 2017

Social dilemmas have been regarded as the essence of evolution game theory, in which the prisoner's dilemma game is the most famous metaphor for the problem of cooperation. Recent findings revealed people's behavior violated the Sure Thing Principle in such games. Classic probability methodologies have difficulty explaining the underlying mechanisms of people's behavior. In this paper, a novel quantum-like Bayesian Network was proposed to accommodate the paradoxical phenomenon. The special network can take interference into consideration, which is likely to be an efficient way to describe the underlying mechanism. With the assistance of belief entropy, named as Deng entropy, the paper proposes Belief Distance to render the model practical. Tested with empirical data, the proposed model is proved to be predictable and effective.
Defend against advanced persistent threats: An optimal control approach
Venue: arXiv cs.CR 2017
Year: 2017

The new cyber attack pattern of advanced persistent threat (APT) has posed a serious threat to modern society. This paper addresses the APT defense problem, i.e., the problem of how to effectively defend against an APT campaign. Based on a novel APT attack-defense model, the effectiveness of an APT defense strategy is quantified. Thereby, the APT defense problem is modeled as an optimal control problem, in which an optimal control stands for a most effective APT defense strategy. The existence of an optimal control is proved, and an optimality system is derived. Consequently, an optimal control can be figured out by solving the optimality system. Some examples of the optimal control are given. Finally, the influence of some factors on the effectiveness of an optimal control is examined through computer experiments. These findings help organizations to work out policies of defending against APTs.